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Classification of multichannel EEG patterns using parallel hidden Markov models

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Abstract

In this paper, a parallel hidden-Markov-model (PHMM)-based approach is proposed for the problem of multichannel electroencephalogram (EEG) patterns classification. The approach is based on multi-channel representation of the EEG signals using a parallel combination of HMMs, where each model represents a particular channel. The performance of the proposed algorithm is studied using an artificial EEG database, and two real EEG databases: a database of two classes of EEGs elicited during a task of imagery of hand upward and downward movements of a computer screen cursor (db Ia), and a database of two classes of sensorimotor EEGs elicited during a feedback-regulated left–right motor imagery task (db III). The results show that the proposed algorithm outperforms other commonly used methods with classification rate improvement of 2 and 10% for db Ia and db III, respectively. In addition, the proposed method outperforms a support vector machine classifier with a linear kernel, when both classifiers utilize the same feature set. The results also show that a model architecture which includes a left-to-right scheme with no skips, five states and three Gaussians, outperforms the other tested architectures due to the fact that it allows a better modeling of the temporal sequencing of the EEG components.

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Acknowledgments

This work was initiated by Prof. Arnon Cohen, head of the signal processing laboratory at Ben-Gurion University of the Negev who passed away during the project. By completing this work we continue his legacy of honest and fair research.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel

    Dror Lederman & Joseph Tabrikian

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  1. Dror Lederman
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  2. Joseph Tabrikian
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Correspondence to Dror Lederman.

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Lederman, D., Tabrikian, J. Classification of multichannel EEG patterns using parallel hidden Markov models. Med Biol Eng Comput 50, 319–328 (2012). https://doi.org/10.1007/s11517-012-0871-2

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  • DOI: https://doi.org/10.1007/s11517-012-0871-2

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